Tomorrow we’ll be attending The Hack Summit where we’ll be sharing insightful knowledge on how  Oxla can level up your cybersecurity game! Come say hi at our booth! 📆Date: Tomorrow! (October 18, 2024) 📍Location: PGE Narodowy See you there!👋🏼

Continuing Oxla's event tour, our team will host a booth at RetailTec Congress next week! Our Founder & CTO, Adam Szymański, will also be giving a session titled 'The Future of Big Data in Retail: How to Reduce Data Processing Costs.' Looking forward to the event!

Here's a little throwback to The Hack Summit two weeks ago. We had so many interesting conversations with incredibly knowledgeable and friendly people all day long!

Greetings from Web Summit! Stop by stand G131 to find out how you can slash compute costs for distributed analytics with our high-performance analytical database 🤓

The latest ClickBench update ranks Oxla no. 6 on c6a.4xlarge 500GB gp2 when compared to popular analytical DBs and engines, demonstrating top-tier performance for self-hosted analytics 💪 #OLAP

Cloud warehousing seems fair—pay for what you use, no infra worries. But you're paying a steep premium on overpriced public cloud compute. CPUs got faster and cheaper. Cloud prices barely budged. Self-hosting is simpler (and cheaper) than you'd think #analytics #OLAP #database

Can you really own data in your cloud warehouse? Even with regional hosting, the CLOUD Act lets U.S. authorities request data from AWS, GCP, or Azure regardless of location. For real control, on-prem is the best option, and it doesn't mean sacrificing scalability or performance

On-prem OLAP shouldn’t come with cloud-native complexity. But most modern OLAP systems were built for the cloud. Oxla is different. A recent customer POC? Expected setup: 2 weeks. Actual: 17 minutes on a call with our support engineer. #datawarehouse #OLAP #dataengineering

Our second patent covers a novel approach to using hash tables for processing queries. For data engineers, this means faster queries, lower infrastructure costs, and a more efficient way to handle datasets at terabyte scale and beyond. #dataengineering #dataanalytics #database